Smart Finance Investment Management and Community Powered by Generative AI

The smart Finance Investment Management and Community powered by using Generative AI is a revolutionary platform that leverages AI-pushed insights to help clients make knowledgeable economic selections. At the center of our device is FinLit, a generative AI model making use of prolonged quicktime period memory (LSTM) networks to offer customized funding tips based on individual demographics, together with gender, age, financial expertise, income, and investment period. The platform is educated and examined with the aid of the usage of an IBM economic dataset to make certain accuracy and reliability. Moreover, we combine a cost Tracker that visually represents customers’ spending patterns through a pie chart categorizing expenses (e.g., lease $30 \%$, financial savings $20 \%$). FinLit, similarly, analyzes these styles to imply regions in which customers can optimize their spending. To foster a collaborative financial environment, we introduce a network discussion board wherein clients can present insights, interact in discussions, and interact via likes and replies. Moreover, our financial sources phase offers direct links to numerous investment schemes, which include Public Provident Fund (PPF), steady Deposits (FD), countrywide savings certificate (NSC), and United States of America-huge Pension device (NPS), among others. Finally, to beautify financial literacy, we’ve got an amusing Quiz designed to educate and interact with users, helping them in improving their understanding of economic thoughts interactively. productivity and task delight.

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Publication Details

Published
2025-08-07
DOI
https://doi.org/10.1109/iccpct65132.2025.11176580
Citations
1
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
2.28
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article

Smart Finance Investment Management and Community Powered by Generative AI

Ramya Kuppusamy, A. Senthilselvi, Aruhn Venkat, Lakshmi Kumar et al.
1 citations
FinTech, Crowdfunding, Digital Finance
2.28
article

Smart Finance Investment Management and Community Powered by Generative AI

Ramya Kuppusamy, A. Senthilselvi, Aruhn Venkat, Lakshmi Kumar, V Charan Sangeet, Nagarapu Madhuri
article en
1 citations

Abstract

The smart Finance Investment Management and Community powered by using Generative AI is a revolutionary platform that leverages AI-pushed insights to help clients make knowledgeable economic selections. At the center of our device is FinLit, a generative AI model making use of prolonged quicktime period memory (LSTM) networks to offer customized funding tips based on individual demographics, together with gender, age, financial expertise, income, and investment period. The platform is educated and examined with the aid of the usage of an IBM economic dataset to make certain accuracy and reliability. Moreover, we combine a cost Tracker that visually represents customers’ spending patterns through a pie chart categorizing expenses (e.g., lease $30 \%$, financial savings $20 \%$). FinLit, similarly, analyzes these styles to imply regions in which customers can optimize their spending. To foster a collaborative financial environment, we introduce a network discussion board wherein clients can present insights, interact in discussions, and interact via likes and replies. Moreover, our financial sources phase offers direct links to numerous investment schemes, which include Public Provident Fund (PPF), steady Deposits (FD), countrywide savings certificate (NSC), and United States of America-huge Pension device (NPS), among others. Finally, to beautify financial literacy, we’ve got an amusing Quiz designed to educate and interact with users, helping them in improving their understanding of economic thoughts interactively. productivity and task delight.

SRM Dental College (IN), Aditya Birla (India) (IN)
Openalex Percentile: Top 9%
FinTech, Crowdfunding, Digital Finance
2.28
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Smart Finance Investment Management and Community Powered by Generative AI — Ramya Kuppusamy, A. Senthilselvi, et al. · (2025) | TGRS Research Map | TGRS